{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os\n",
    "directorys=['LTH_syn_plot']  # temp models\n",
    "for directory in directorys:\n",
    "    if not os.path.exists(directory):\n",
    "        os.makedirs(directory)\n",
    "result=np.load('result.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=result.reshape(50,int(len(result)/50))\n",
    "MaskLow,MaskUp,Maskmean,Ntr=[],[],[],[]\n",
    "for j in range(25):\n",
    "    i=1\n",
    "    MaskLow.append(np.mean(df[i*5:i*5+5,j])-min(df[i*5:i*5+5,j]))\n",
    "    MaskUp.append(max(df[i*5:i*5+5,j])-np.mean(df[i*5:i*5+5,j]))\n",
    "    #FCLow.append(np.mean(df[i*5:i*5+5,2])-min(df[i*5:i*5+5,2]))\n",
    "    #FCUp.append(max(df[i*5:i*5+5,2])-np.mean(df[i*5:i*5+5,2]))\n",
    "    Maskmean.append(np.mean(df[i*5:i*5+5,j]))\n",
    "    #FCmean.append(np.mean(df[i*5:i*5+5,2]))\n",
    "    Ntr.append(0.8**j*100)\n",
    "Maskerr= np.concatenate((np.array(MaskLow).reshape(1,len(MaskLow)),np.array(MaskUp).reshape(1,len(MaskUp))),axis=0)\n",
    "    #FCerr=np.concatenate((np.array(FCLow).reshape(1,len(FCLow)),np.array(FCUp).reshape(1,len(FCUp))),axis=0)\n",
    "fig, ax = plt.subplots()\n",
    "#ax.errorbar(Ntr, FCmean, xerr=100, yerr=FCerr,capthick=4)\n",
    "ax.errorbar(Ntr, Maskmean, xerr=0, yerr=Maskerr)\n",
    "    #plt.legend(['OverparaFC','ParaACE'])\n",
    "plt.xlabel('Percents of Weights Remaining')\n",
    "plt.ylabel('Test NRMSE')   \n",
    "plt.title('Synthetic dataset '+str(i+1)+', LTH') \n",
    "plt.xscale('log')\n",
    "ax.invert_xaxis()\n",
    "plt.xticks([0.5,1,5,20,50,100],[0.5,1,5,20,50,100])\n",
    "plt.savefig('LTH_syn_plot/LTHsyn'+str(i+1)+'.pdf',bbox_inches='tight',dpi='figure',pad_inches=0.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.026899707\n",
      "0.043760933\n",
      "0.032375075\n",
      "0.032312255\n",
      "0.038636867\n",
      "0.033031326\n",
      "0.01812317\n",
      "0.021124717\n",
      "0.029177096\n",
      "0.024782972\n"
     ]
    }
   ],
   "source": [
    "for dataset in range(10):\n",
    "    print(np.mean(df[dataset*5:dataset*5+5,13]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03002241"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df[:,13])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tianjian",
   "language": "python",
   "name": "tianjian"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
